Faculty Publications
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Item An Improved Noise Reduction Technique for Enhancing the Intelligibility of Sinewave Vocoded Speech: Implication in Cochlear Implants(Institute of Electrical and Electronics Engineers Inc., 2023) Poluboina, V.; Pulikala, A.; Pitchaimuthu, A.N.P.A cochlear implant (CI) is the most suitable option for individuals with severe profound hearing loss. CI restores the audibility to near perfection and offers good speech understanding in quiet. However, the speech perception in noise with CIs is less optimal as most speech coding strategies of CIs encode only the temporal envelope. Besides the current CI signal coding strategies lacks sophisticated pre-processing. In the current study, we proposed a novel pre-processing method to improve speech Intelligibility in noise and tested using the acoustic simulations of cochlear implants. The proposed noise reduction technique aims to minimize the mean square error (MSE) between the temporal envelopes of the enhanced speech and its clean speech. Therefore, the proposed method will be suitable for CI applications. This paper provides an analysis of the theoretical derivation of the noise suppression function and also the performance evaluation using objective and subjective tests. The effectiveness of the proposed method was objectively evaluated using the SRMR-CI and ESTOI. Additionally, speech recognition through the acoustic simulations of the cochlear implant was done for the subjective evaluation. Performance of the proposed method was compared with the Weiner filter (WF) and sigmoidal functions. The sinewave vocoder was used to simulate the cochlear implant perception. Both objective and subjective scores revealed that the performance of the proposed technique is superior to the WF and sigmoidal function. © 2013 IEEE.Item Deep Speech Denoising with Minimal Dependence on Clean Speech Data(Birkhauser, 2024) Poluboina, V.; Pulikala, A.; Pitchaimuthu, A.N.Most of the existing deep learning-based speech denoising methods rely heavily on clean speech data. According to the traditional view, a large number of noisy and clean speech samples are required for good speech denoising performance. However, the data collection is a technical barrier to this criteria, particularly in economically challenged areas and for languages with limited resources. Training deep denoising networks with only noisy speech samples is a viable option to avoid dependence on sample data size. In this study, the target and input of a DCU-Net were trained using only noisy speech samples. Experimental results demonstrate that, when compared to traditional speech denoising techniques, the proposed approach avoids not only the high dependence on clean targets but also the high dependence on large data sizes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.